OpenRaiser/Intern-Atlas huggingface dataset:
Automatic Brain Structures Segmentation Using Deep Residual Dilated U-Net
Venue: arXiv cs.CV 2018
Year: 2018

Brain image segmentation is used for visualizing and quantifying anatomical structures of the brain. We present an automated ap-proach using 2D deep residual dilated networks which captures rich context information of different tissues for the segmentation of eight brain structures. The proposed system was evaluated in the MICCAI Brain Segmentation Challenge and ranked 9th out of 22 teams. We further compared the method with traditional U-Net using leave-one-subject-out cross-validation setting on the public dataset. Experimental results shows that the proposed method outperforms traditional U-Net (i.e. 80.9% vs 78.3% in averaged Dice score, 4.35mm vs 11.59mm in averaged robust Hausdorff distance) and is computationally efficient.
Software Conceptual Integrity: Deconstruction, Then Reconstruction
Venue: arXiv cs.SE 2018
Year: 2018

Conceptual Integrity is the most important consideration for software system design, as stated by Frederick Brooks. Brooks also suggested that Conceptual Integrity can be attained by means of design principles, such as Propriety, and Orthogonality. However, Brooks' principles have not been formalized, posing obstacles to their application in practice, and to a deeper comprehension of Conceptual Integrity. This paper has three goals: first, to achieve deeper comprehension of Conceptual Integrity by deconstructing it into two phases, viz. Conceptualization and Modularization, iteratively applied during software system design; second, to show that the algebraic Linear Software Models already provide the hitherto lacking formalization of Brooks' design principles, which surprisingly belong mainly to the Modularization phase; third, to reconstruct Conceptualization and Modularization, preserving the desirable tension between: a- phases separation, each with its own specific formal manipulation techniques; b- precise transition between these phases, consisting of explicit mutual relationships. The tension stems from the Modularity Matrix linking two very different kinds of entities - system concepts and abstract mathematical constructs - as seldom linked before. The paper motivates the two software design phases, illustrating Conceptualization with examples, and characterizing Modularization by its more mature mathematical theory.
Explaining Deep Learning Models using Causal Inference
Venue: arXiv cs.LG 2018
Year: 2018

Although deep learning models have been successfully applied to a variety of tasks, due to the millions of parameters, they are becoming increasingly opaque and complex. In order to establish trust for their widespread commercial use, it is important to formalize a principled framework to reason over these models. In this work, we use ideas from causal inference to describe a general framework to reason over CNN models. Specifically, we build a Structural Causal Model (SCM) as an abstraction over a specific aspect of the CNN. We also formulate a method to quantitatively rank the filters of a convolution layer according to their counterfactual importance. We illustrate our approach with popular CNN architectures such as LeNet5, VGG19, and ResNet32.
HSD-CNN: Hierarchically self decomposing CNN architecture using class specific filter sensitivity analysis
Venue: arXiv cs.CV 2018
Year: 2018

Conventional Convolutional neural networks (CNN) are trained on large domain datasets and are hence typically over-represented and inefficient in limited class applications. An efficient way to convert such large many-class pre-trained networks into small few-class networks is through a hierarchical decomposition of its feature maps. To alleviate this issue, we propose an automated framework for such decomposition in Hierarchically Self Decomposing CNN (HSD-CNN), in four steps. HSD-CNN is derived automatically using a class-specific filter sensitivity analysis that quantifies the impact of specific features on a class prediction. The decomposed hierarchical network can be utilized and deployed directly to obtain sub-networks for a subset of classes, and it is shown to perform better without the requirement of retraining these sub-networks. Experimental results show that HSD-CNN generally does not degrade accuracy if the full set of classes are used. Interestingly, when operating on known subsets of classes, HSD-CNN has an improvement in accuracy with a much smaller model size, requiring much fewer operations. HSD-CNN flow is verified on the CIFAR10, CIFAR100 and CALTECH101 data sets. We report accuracies up to $85.6\%$ ( $94.75\%$ ) on scenarios with 13 ( 4 ) classes of CIFAR100, using a pre-trained VGG-16 network on the full data set. In this case, the proposed HSD-CNN requires $3.97 \times$ fewer parameters and has $71.22\%$ savings in operations, in comparison to baseline VGG-16 containing features for all 100 classes.
Recognizing generating subgraphs revisited
Venue: arXiv cs.DM 2018
Year: 2018

A graph $G$ is well-covered if all its maximal independent sets are of the same cardinality. Assume that a weight function $w$ is defined on its vertices. Then $G$ is $w$-well-covered if all maximal independent sets are of the same weight. For every graph $G$, the set of weight functions $w$ such that $G$ is $w$-well-covered is a vector space, denoted as $WCW(G).$ Deciding whether an input graph $G$ is well-covered is co-NP-complete. Therefore, finding $WCW(G)$ is co-NP-hard. A generating subgraph of a graph $G$ is an induced complete bipartite subgraph $B$ of $G$ on vertex sets of bipartition $B_{X}$ and $B_{Y}$, such that each of $S \cup B_{X}$ and $S \cup B_{Y}$ is a maximal independent set of $G$, for some independent set $S$. If $B$ is generating, then $w(B_{X})=w(B_{Y})$ for every weight function $w \in WCW(G)$. Therefore, generating subgraphs play an important role in finding $WCW(G)$. The decision problem whether a subgraph of an input graph is generating is known to be NP-complete. In this article, we prove NP-completeness of the problem for graphs without cycles of length 3 and 5, and for bipartite graphs with girth at least 6. On the other and, we supply polynomial algorithms for recognizing generating subgraphs and finding $WCW(G)$, when the input graph is bipartite without cycles of length 6. We also present a polynomial algorithm which finds $WCW(G)$ when $G$ does not contain cycles of lengths 3, 4, 5, and 7.
Managing App Install Ad Campaigns in RTB: A Q-Learning Approach
Venue: arXiv cs.GT 2018
Year: 2018

Real time bidding (RTB) enables demand side platforms (bidders) to scale ad campaigns across multiple publishers affiliated to an RTB ad exchange. While driving multiple campaigns for mobile app install ads via RTB, the bidder typically has to: (i) maintain each campaign's efficiency (i.e., meet advertiser's target cost-per-install), (ii) be sensitive to advertiser's budget, and (iii) make profit after payouts to the ad exchange. In this process, there is a sense of delayed rewards for the bidder's actions; the exchange charges the bidder right after the ad is shown, but the bidder gets to know about resultant installs after considerable delay. This makes it challenging for the bidder to decide beforehand the bid (and corresponding cost charged to advertiser) for each ad display opportunity. To jointly handle the objectives mentioned above, we propose a state space based policy which decides the exchange bid and advertiser cost for each opportunity. The state space captures the current efficiency, budget utilization and profit. The policy based on this state space is trained on past decisions and outcomes via a novel Q-learning algorithm which accounts for the delay in install notifications. In our experiments based on data from app install campaigns managed by Yahoo's Gemini advertising platform, the Q-learning based policy led to a significant increase in the profit and number of efficient campaigns.
Multiple Subspace Alignment Improves Domain Adaptation
Venue: arXiv cs.CV 2018
Year: 2018

We present a novel unsupervised domain adaptation (DA) method for cross-domain visual recognition. Though subspace methods have found success in DA, their performance is often limited due to the assumption of approximating an entire dataset using a single low-dimensional subspace. Instead, we develop a method to effectively represent the source and target datasets via a collection of low-dimensional subspaces, and subsequently align them by exploiting the natural geometry of the space of subspaces, on the Grassmann manifold. We demonstrate the effectiveness of this approach, using empirical studies on two widely used benchmarks, with state of the art domain adaptation performance
Analysis vs Synthesis with Structure - An Investigation of Union of Subspace Models on Graphs
Venue: arXiv cs.DM 2018
Year: 2018

We consider the problem of characterizing the `duality gap' between sparse synthesis- and cosparse analysis-driven signal models through the lens of spectral graph theory, in an effort to comprehend their precise equivalencies and discrepancies. By detecting and exploiting the inherent connectivity structure, and hence, distinct set of properties, of rank-deficient graph difference matrices such as the graph Laplacian, we are able to substantiate discrepancies between the cosparse analysis and sparse synthesis models, according to which the former constitutes a constrained and translated instance of the latter. In view of a general union of subspaces model, we conduct a study of the associated subspaces and their composition, which further facilitates the refinement of specialized uniqueness and recovery guarantees, and discover an underlying structured sparsity model based on the graph incidence matrix. Furthermore, for circulant graphs, we provide an exact characterization of underlying subspaces by deriving closed-form expressions as well as demonstrating transitional properties between equivalence and non-equivalence for a parametric generalization of the graph Laplacian.
Road Damage Detection And Classification In Smartphone Captured Images Using Mask R-CNN
Venue: arXiv cs.CV 2018
Year: 2018

This paper summarizes the design, experiments and results of our solution to the Road Damage Detection and Classification Challenge held as part of the 2018 IEEE International Conference On Big Data Cup. Automatic detection and classification of damage in roads is an essential problem for multiple applications like maintenance and autonomous driving. We demonstrate that convolutional neural net based instance detection and classfication approaches can be used to solve this problem. In particular we show that Mask-RCNN, one of the state-of-the-art algorithms for object detection, localization and instance segmentation of natural images, can be used to perform this task in a fast manner with effective results. We achieve a mean F1 score of 0.528 at an IoU of 50% on the task of detection and classification of different types of damages in real-world road images acquired using a smartphone camera and our average inference time for each image is 0.105 seconds on an NVIDIA GeForce 1080Ti graphic card. The code and saved models for our approach can be found here : https://github.com/sshkhr/BigDataCup18 Submission
Time synchronization in vehicular ad-hoc networks: A survey ontheory and practice
Venue: arXiv cs.NI 2018
Year: 2018

Time synchronization in communication networks provides a common time frame among all nodes, thussupporting various network functions such as message transmission, channel scheduling and resourcesharing in real-time and in correct order. In vehicular ad-hoc networks (VANETs) for connected andautomated vehicles, network nodes must be synchronized to exchange time-critical vehicle locationsand warning messages for various road safety applications. However, this is challenging as the datacommunication systems need to achieve low latency and high reliability under the conditions ofhigh user dynamics and density. While many synchronization techniques have been developed forgeneral communications networks, it is necessary to understand the requirements for VANET timesynchronization and the applicability of existing time synchronization techniques in VANET applications.This paper provides a survey on theory and practice of time synchronization in VANETs. It addressessome key factors in VANET time synchronization such as requirements analysis, precision, accuracy,availability, scalability and compatibility, and highlights the advantages of Global Navigation SatelliteSystem (GNSS) in VANET time synchronization. Through this survey, some insights are developed intoexisting and emerging protocols for time synchronization in VANETs.
Adversarial Learning of Label Dependency: A Novel Framework for Multi-class Classification
Venue: arXiv cs.LG 2018
Year: 2018

Recent work has shown that exploiting relations between labels improves the performance of multi-label classification. We propose a novel framework based on generative adversarial networks (GANs) to model label dependency. The discriminator learns to model label dependency by discriminating real and generated label sets. To fool the discriminator, the classifier, or generator, learns to generate label sets with dependencies close to real data. Extensive experiments and comparisons on two large-scale image classification benchmark datasets (MS-COCO and NUS-WIDE) show that the discriminator improves generalization ability for different kinds of models
Classifying Patent Applications with Ensemble Methods
Venue: arXiv cs.CL 2018
Year: 2018

We present methods for the automatic classification of patent applications using an annotated dataset provided by the organizers of the ALTA 2018 shared task - Classifying Patent Applications. The goal of the task is to use computational methods to categorize patent applications according to a coarse-grained taxonomy of eight classes based on the International Patent Classification (IPC). We tested a variety of approaches for this task and the best results, 0.778 micro-averaged F1-Score, were achieved by SVM ensembles using a combination of words and characters as features. Our team, BMZ, was ranked first among 14 teams in the competition.
Automatically Generate Steganographic Text Based on Markov Model and Huffman Coding
Venue: arXiv cs.CR 2018
Year: 2018

Steganography, as one of the three basic information security systems, has long played an important role in safeguarding the privacy and confidentiality of data in cyberspace. The text is the most widely used information carrier in people's daily life, using text as a carrier for information hiding has broad research prospects. However, due to the high coding degree and less information redundancy in the text, it has been an extremely challenging problem to hide information in it for a long time. In this paper, we propose a steganography method which can automatically generate steganographic text based on the Markov chain model and Huffman coding. It can automatically generate fluent text carrier in terms of secret information which need to be embedded. The proposed model can learn from a large number of samples written by people and obtain a good estimate of the statistical language model. We evaluated the proposed model from several perspectives. Experimental results show that the performance of the proposed model is superior to all the previous related methods in terms of information imperceptibility and information hidden capacity.
Learning Representations of Missing Data for Predicting Patient Outcomes
Venue: arXiv cs.LG 2018
Year: 2018

Extracting actionable insight from Electronic Health Records (EHRs) poses several challenges for traditional machine learning approaches. Patients are often missing data relative to each other; the data comes in a variety of modalities, such as multivariate time series, free text, and categorical demographic information; important relationships among patients can be difficult to detect; and many others. In this work, we propose a novel approach to address these first three challenges using a representation learning scheme based on message passing. We show that our proposed approach is competitive with or outperforms the state of the art for predicting in-hospital mortality (binary classification), the length of hospital visits (regression) and the discharge destination (multiclass classification).
SLAM-Assisted Coverage Path Planning for Indoor LiDAR Mapping Systems
Venue: arXiv cs.RO 2018
Year: 2018

Applications involving autonomous navigation and planning of mobile agents can benefit greatly by employing online Simultaneous Localization and Mapping (SLAM) techniques, however, their proper implementation still warrants an efficient amalgamation with any offline path planning method that may be used for the particular application. In this paper, such a case of amalgamation is considered for a LiDAR-based indoor mapping system which presents itself as a 2D coverage path planning problem implemented along with online SLAM. This paper shows how classic offline Coverage Path Planning (CPP) can be altered for use with online SLAM by proposing two modifications: (i) performing convex decomposition of the polygonal coverage area to allow for an arbitrary choice of an initial point while still tracing the shortest coverage path and (ii) using a new approach to stitch together the different cells within the polygonal area to form a continuous coverage path. Furthermore, an alteration to the SLAM operation to suit the coverage path planning strategy is also made that evaluates navigation errors in terms of an area coverage cost function. The implementation results show how the combination of the two modified offline and online planning strategies allow for an improvement in the total area coverage by the mapping system - the modification thus presents an approach for modifying offline and online navigation strategies for robust operation.
Pareto-Optimal Allocation of Indivisible Goods with Connectivity Constraints
Venue: arXiv cs.GT 2018
Year: 2018

We study the problem of allocating indivisible items to agents with additive valuations, under the additional constraint that bundles must be connected in an underlying item graph. Previous work has considered the existence and complexity of fair allocations. We study the problem of finding an allocation that is Pareto-optimal. While it is easy to find an efficient allocation when the underlying graph is a path or a star, the problem is NP-hard for many other graph topologies, even for trees of bounded pathwidth or of maximum degree 3. We show that on a path, there are instances where no Pareto-optimal allocation satisfies envy-freeness up to one good, and that it is NP-hard to decide whether such an allocation exists, even for binary valuations. We also show that, for a path, it is NP-hard to find a Pareto-optimal allocation that satisfies maximin share, but show that a moving-knife algorithm can find such an allocation when agents have binary valuations that have a non-nested interval structure.
Unseen Word Representation by Aligning Heterogeneous Lexical Semantic Spaces
Venue: arXiv cs.CL 2018
Year: 2018

Word embedding techniques heavily rely on the abundance of training data for individual words. Given the Zipfian distribution of words in natural language texts, a large number of words do not usually appear frequently or at all in the training data. In this paper we put forward a technique that exploits the knowledge encoded in lexical resources, such as WordNet, to induce embeddings for unseen words. Our approach adapts graph embedding and cross-lingual vector space transformation techniques in order to merge lexical knowledge encoded in ontologies with that derived from corpus statistics. We show that the approach can provide consistent performance improvements across multiple evaluation benchmarks: in-vitro, on multiple rare word similarity datasets, and in-vivo, in two downstream text classification tasks.
Blindfold Baselines for Embodied QA
Venue: arXiv cs.CV 2018
Year: 2018

We explore blindfold (question-only) baselines for Embodied Question Answering. The EmbodiedQA task requires an agent to answer a question by intelligently navigating in a simulated environment, gathering necessary visual information only through first-person vision before finally answering. Consequently, a blindfold baseline which ignores the environment and visual information is a degenerate solution, yet we show through our experiments on the EQAv1 dataset that a simple question-only baseline achieves state-of-the-art results on the EmbodiedQA task in all cases except when the agent is spawned extremely close to the object.
Finding All Bayesian Network Structures within a Factor of Optimal
Venue: arXiv cs.AI 2018
Year: 2018

A Bayesian network is a widely used probabilistic graphical model with applications in knowledge discovery and prediction. Learning a Bayesian network (BN) from data can be cast as an optimization problem using the well-known score-and-search approach. However, selecting a single model (i.e., the best scoring BN) can be misleading or may not achieve the best possible accuracy. An alternative to committing to a single model is to perform some form of Bayesian or frequentist model averaging, where the space of possible BNs is sampled or enumerated in some fashion. Unfortunately, existing approaches for model averaging either severely restrict the structure of the Bayesian network or have only been shown to scale to networks with fewer than 30 random variables. In this paper, we propose a novel approach to model averaging inspired by performance guarantees in approximation algorithms. Our approach has two primary advantages. First, our approach only considers credible models in that they are optimal or near-optimal in score. Second, our approach is more efficient and scales to significantly larger Bayesian networks than existing approaches.
Neuroimaging Modality Fusion in Alzheimer's Classification Using Convolutional Neural Networks
Venue: arXiv cs.LG 2018
Year: 2018

Automated methods for Alzheimer's disease (AD) classification have the potential for great clinical benefits and may provide insight for combating the disease. Machine learning, and more specifically deep neural networks, have been shown to have great efficacy in this domain. These algorithms often use neurological imaging data such as MRI and PET, but a comprehensive and balanced comparison of these modalities has not been performed. In order to accurately determine the relative strength of each imaging variant, this work performs a comparison study in the context of Alzheimer's dementia classification using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Furthermore, this work analyzes the benefits of using both modalities in a fusion setting and discusses how these data types may be leveraged in future AD studies using deep learning.
A Variational Inference based Detection Method for Repetition Coded Generalized Spatial Modulation
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we consider a simple coding scheme for spatial modulation (SM), where the same set of active transmit antennas is repeatedly used over consecutive multiple transmissions. Based on a Gaussian approximation, an approximate maximum likelihood (ML) detection problem is formulated to detect the indices of active transmit antennas. We show that the solution to the approximate ML detection problem can achieve a full coding gain. Furthermore, we develop a low-complexity iterative algorithm to solve the problem with low complexity based on a well-known machine learning approach, i.e., variational inference. Simulation results show that the proposed algorithm can have a near ML performance. A salient feature of the proposed algorithm is that its complexity is independent of the number of active transmit antennas, whereas an exhaustive search for the ML problem requires a complexity that grows exponentially with the number of active transmit antennas.